To send OpenTelemetry metrics and traces generated by ARMS from your AI Application to an OpenTelemetry Collector, follow the below steps.

The OpenTelemetry Collector is a vendor-agnostic way to receive, process, and export telemetry data. It can act as an intermediary to route your ARMS data to multiple backends or apply processing transformations.

1. Deploy OpenTelemetry Collector

Install the Collector (choose your preferred method):

Docker

# Run OpenTelemetry Collector with OTLP receivers
docker run -p 4317:4317 -p 4318:4318 \
  -v $(pwd)/otel-collector-config.yaml:/etc/otelcol-contrib/config.yaml \
  otel/opentelemetry-collector-contrib:latest

Kubernetes

# Deploy using Helm
helm repo add open-telemetry https://open-telemetry.github.io/opentelemetry-helm-charts
helm install my-otel-collector open-telemetry/opentelemetry-collector \
  --set config.receivers.otlp.protocols.grpc.endpoint="0.0.0.0:4317" \
  --set config.receivers.otlp.protocols.http.endpoint="0.0.0.0:4318"

Binary

# Download and run the collector binary
curl -LO https://github.com/open-telemetry/opentelemetry-collector-releases/releases/latest/download/otelcol-contrib_linux_amd64.tar.gz
tar -xzf otelcol-contrib_linux_amd64.tar.gz
./otelcol-contrib --config=otel-collector-config.yaml

Basic Collector Configuration: Create an otel-collector-config.yaml file:

Basic OTLP Configuration

receivers:
  otlp:
    protocols:
      grpc:
        endpoint: 0.0.0.0:4317
      http:
        endpoint: 0.0.0.0:4318

processors:
  batch:

exporters:
  logging:
    loglevel: debug
  # Add your preferred backend exporters here
  # Examples: jaeger, prometheus, otlp, etc.

service:
  pipelines:
    traces:
      receivers: [otlp]
      processors: [batch]
      exporters: [logging]
    metrics:
      receivers: [otlp]
      processors: [batch]
      exporters: [logging]

Get the Collector Endpoint:

• Default HTTP endpoint: http://localhost:4318 or http://your-collector-host:4318
• Default gRPC endpoint: http://localhost:4317 or http://your-collector-host:4317

2. Instrument your application

SDK

For direct integration into your Python applications:

Function Arguments

import elsai_arms

elsai_arms.init(
  otlp_endpoint="YOUR_COLLECTOR_ENDPOINT"
)

Replace:

1. YOUR_COLLECTOR_ENDPOINT with your OpenTelemetry Collector endpoint from Step 1.
   • Local HTTP: http://localhost:4318
   • Local gRPC: http://localhost:4317
   • Remote: http://your-collector-host:4318
   • Kubernetes: http://my-otel-collector:4318

Environment Variables

import elsai_arms

elsai_arms.init()

Set these environment variables:

export OTEL_EXPORTER_OTLP_ENDPOINT="YOUR_COLLECTOR_ENDPOINT"
export OTEL_SERVICE_NAME="my-ai-service"
export OTEL_DEPLOYMENT_ENVIRONMENT="production"

Replace:

1. YOUR_COLLECTOR_ENDPOINT with your OpenTelemetry Collector endpoint from Step 1.

See the ARMS SDK configuration docs for more advanced options.

CLI

For zero-code auto-instrumentation via command line:

CLI Arguments

# Using CLI arguments
elsai-arms-instrument \
  --otlp-endpoint "YOUR_COLLECTOR_ENDPOINT" \
  --service-name "my-ai-service" \
  --deployment-environment "production" \
  python app.py

Replace:

1. YOUR_COLLECTOR_ENDPOINT with your OpenTelemetry Collector endpoint from Step 1.

Environment Variables

# Set environment variables (takes precedence over CLI args)
export OTEL_EXPORTER_OTLP_ENDPOINT="YOUR_COLLECTOR_ENDPOINT"
export OTEL_SERVICE_NAME="my-ai-service"
export OTEL_DEPLOYMENT_ENVIRONMENT="production"

# Run your application
elsai-arms-instrument python app.py

Replace:

1. YOUR_COLLECTOR_ENDPOINT with your OpenTelemetry Collector endpoint from Step 1.

See the ARMS SDK configuration docs for more advanced options.

3. Configure Collector Exporters

Once your LLM application is sending data to the OpenTelemetry Collector, configure exporters to send data to your preferred observability backends:

Popular Exporter Configurations:

Jaeger (Traces)

exporters:
  jaeger:
    endpoint: http://jaeger-collector:14250
    tls:
      insecure: false

service:
  pipelines:
    traces:
      receivers: [otlp]
      processors: [batch]
      exporters: [jaeger]

Prometheus (Metrics)

exporters:
  prometheus:
    endpoint: "0.0.0.0:8889"
    metric_expiration: 180m

service:
  pipelines:
    metrics:
      receivers: [otlp]
      processors: [batch]
      exporters: [prometheus]

Multiple Backends

exporters:
  otlp/backend1:
    endpoint: http://backend1:4317
  otlp/backend2:
    endpoint: http://backend2:4317
  logging:
    loglevel: debug

service:
  pipelines:
    traces:
      receivers: [otlp]
      processors: [batch]
      exporters: [otlp/backend1, otlp/backend2, logging]
    metrics:
      receivers: [otlp]
      processors: [batch]
      exporters: [otlp/backend1, otlp/backend2]

Monitor Collector Health:

# Check collector logs
docker logs <collector-container-id>

# Or for Kubernetes
kubectl logs -l app.kubernetes.io/name=opentelemetry-collector

# Health check endpoint (if enabled)
curl http://localhost:13133/

Benefits of Using OpenTelemetry Collector:

• Vendor Agnostic: Route data to multiple backends simultaneously
• Data Processing: Apply transformations, filtering, and sampling
• Protocol Translation: Convert between different telemetry formats
• Buffering & Reliability: Handle network issues and backend outages
• Cost Optimization: Sample and filter data to reduce costs
• Security: Add authentication, encryption, and data anonymization

Your ARMS-instrumented AI applications will send telemetry data to the Collector, which can then process and route it to any number of observability backends, providing flexibility and powerful data processing capabilities for your LLM monitoring infrastructure.